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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Ilmu Komputer dan Informasi Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Kesehatan Lingkungan indonesia Media Statistika JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika WARTA Register: Jurnal Ilmiah Teknologi Sistem Informasi E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) InComTech: Jurnal Telekomunikasi dan Komputer ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JTP - Jurnal Teknologi Pendidikan Indonesian Journal of Community Services Journal of Information Systems and Informatics Jurnal Pustaka Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship Media Pustakawan STATISTIKA PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics Jurnal Ragam Pengabdian Jurnal Informatika IJoICT (International Journal on Information and Communication Technology)
Budi Warsito
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Pengaruh Klasifikasi Sentimen Pada Ulasan Produk Amazon Berbasis Rekayasa Fitur dan K-Nearest Negihbor Putri, Nitami Lestari; Warsito, Budi; Surarso, Bayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117376

Abstract

Ulasan online menjadi faktor penting yang mendorong konsumen untuk membeli barang di e-commerce. Dalam e-commerce, ulasan pelanggan sebelumnya dapat membantu pembeli membuat keputusan yang lebih baik dengan memberikan informasi tentang kualitas produk, kekuatan dan kelemahan, perilaku penjual, harga, dan waktu pengiriman. Namun, keberadaan ulasan palsu menimbulkan tantangan dalam menilai sentimen yang diungkapkan oleh pelanggan asli secara benar. Dalam penelitian ini, berfokus pada analisis sentimen dan bertujuan untuk mengeksplorasi peran sentimen dalam ulasan produk Amazon. Penelitian ini menggunakan kombinasi fitur dari konten ulasan dengan menerapkan klasifikasi K-Nearest Neighbor untuk mengklasifikasikan polaritas sentimen ulasan secara akurat. Dalam mengekstrak skor polaritas dari ulasan, penelitian ini menggunakan pendekatan analisis sentimen berbasis leksikon yaitu Textblob Library dan menetapkan label sentimen dari ulasan produk. Hasil dari pemodelan yang diusulkan mencapai tingkat akurasi sebesar 83% yang menunjukkan keefektifan pemodelan yang diusulkan dalam analisis sentimen. Hasil dari penelitian ini dapat membantu konsumen dalam membuat keputusan pembelian dan membantu penjual dalam meningkatkan nilai produk dan layanan mereka berdasarkan feedback yang diberikan oleh pelanggan.
Enhancing Bank Financial Performance Assessment: A Literature Review of Deep Learning Applications Using the Kitchenham Method Ali, Mahrus; Gernowo, Rahmat; Warsito, Budi; Muthmainah, Faliha
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4224

Abstract

The assessment of bank financial performance is crucial for ensuring the stability of the banking sector. With advancements in technology, especially deep learning (DL), there is increasing potential to improve the accuracy of risk prediction and financial performance evaluation in banks. However, challenges related to data imbalance and model complexity require more efficient approaches. This study aims to examine the application of DL in assessing bank financial performance, with a focus on credit risk, fraud detection, and bankruptcy prediction. A Systematic Literature Review (SLR) was conducted using the Kitchenham approach, analyzing 697 relevant articles to address nine research questions regarding the implementation of DL in the banking sector. This study contributes by providing insights into effective DL models that enhance financial performance and risk prediction in banks, while also offering recommendations for the development of more transparent models. The results indicate that models such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) perform well in handling large financial data. Additionally, hybrid models that combine DL with traditional models demonstrate higher accuracy in bankruptcy prediction and fraud detection.
Assessing the impact of charcoal production activities on the Shea Nut tree vegetation cover Calvin, Esagu John; Warsito, Budi; Hidayat, Jafron Wasiq; Gertrude, Akello; Paul, Gudoyi M; Ahmed, Kamil
Journal of Bioresources and Environmental Sciences Vol 2, No 3 (2023): December 2023
Publisher : BIORE Scientia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbes.2023.19260

Abstract

Charcoal remains the main energy cooking source for urban dwellers in Uganda. The Shea Nut tree produces quality charcoal which is efficient and locally made. Therefore, it is facing increasing threats from the local communities so as to meet the mushrooming demand. The study analyses the state of the Shea Nut tree, drivers of charcoal production, predict Shea Nut tree vegetation coverage, and establish mechanisms for sustainable utilization and conservation of the Shea Nut trees in Kapelebyong District. Landsat images were classified using likelihood classification in ArcGIS and interviews were conducted whilst geospatial, Stata, and Nvivo tools were used for analysis. The findings reflect a sharp declining trend in the coverage of the shea Nut trees by 2.3% and 6% from 2002-2012 and 2012-2022 respectively. The major drivers include high demand from urban areas, the need for income, and unemployment. As a result, it is predicted that by 2032, the coverage will have reduced to only 713 hectares (7.3%) from 1277 hectares (10.6%) in 2022. Therefore, charcoal production with other land uses has greatly resulted in Shea Nut tree deterioration. The study recommends the use of alternative energy sources, the provision of alternative income-generating activities for the local communities, Government of Uganda through NFA needs to enforce the ways through which Shea Nut trees are managed and utilized in order to minimize illegal cutting.
Evaluation of Waste Transportation Routes in Salatiga City Haritsa, Rifda Tsaqifarani; Maryono, Maryono; Rahadian, Rully; Hermawan, Ferry; Warsito, Budi
Jurnal Riset Teknologi Pencegahan Pencemaran Industri Vol. 16 No. 1 (2025): May
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Pencegahan Pencemaran Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The problem of waste transportation is a major challenge in waste management in Salatiga City. With the amount of daily waste generated reaching 457.81 m³ and the volume transported only around 327.33 m³, the level of waste transportation has only reached 71.72%. This study aims to evaluate and optimize the current waste transportation route through a spatial approach using QGIS software. The methods used include field observation, primary and secondary data collection, and spatial analysis of the distribution of routes and workloads of the transport fleet consisting of 9 arm roll units and 1 dump truck unit, with a total average daily trip of 58 trips. The results of the comparison between the existing route and the planned route show a daily route length efficiency of 10.57 km (1.15%), fuel consumption savings of 2.73 liters per day, and travel time efficiency of 25 minutes. The volume of transported waste also increased from 83,730 kg/day to 89,500 kg/day (up 6.89%), which was achieved through more optimal route planning, additional trips to TPS Boja and Tingkir, and equalizing the workload between drivers. The results of this study confirm that GIS-based route optimization can increase the efficiency of distance, fuel, time, and productivity of the waste transportation system as a whole in Salatiga City.
UTAUT-2, HOT-Fit, and PLS-SEM for User Acceptance and Success of the Face Recognition Feature in CAT BKN Application Sari, Juwita Dwinda; Warsito, Budi; Wibowo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.31229

Abstract

Purpose: Face recognition feature was implemented in the National Civil Service Agency's Computer-Assisted Test in 2021. There has been no evaluation of the system's acceptance and success. This study aims to measure user acceptance and evaluate the feature's success using the R Shiny application. Methods: The study utilized 337 respondents from a Google Form-based questionnaire distributed throughout the Regional Office VII of the National Civil Service Agency in Palembang. The hybrid model used was UTAUT-2 and HOT-Fit, with PLS-SEM statistical analysis. Acceptance analysis and feature evaluation were conducted using the developed R Shiny Dashboard. Results: The findings indicated that 15 of the 26 hypotheses were accepted. Behavioral intention and use behavior significantly influence hedonic motivation and habit. User behavior significantly influences user satisfaction, system quality, service quality, information quality, system use, and organizational structure and environment. As users become more familiar with the technology, their experience improves, and system utilization becomes more effective. Novelty: The integration of UTAUT-2 and HOT-Fit models within an R Shiny Dashboard was applied to analyze user acceptance and evaluate the face recognition feature in Computer Computer-Assisted Test selection process. The findings provide recommendations for feature development and improving participant face recognition performance. Moreover, the R Shiny Dashboard can be adapted for user experience analysis and system evaluation in other contexts.
Integration of UTAUT 2 and Delone & McLean to Evaluate Acceptance of Video Conference Application Bayastura, Shahnilna Fitrasha; Warsito, Budi; Nugraheni, Dinar Mutiara Kusumo
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 6 No 2 (2022): August 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v6i2.17897

Abstract

This article explores how college students adopt video conferencing software for distance education. This research aims to examine the factors that influence the spread of video conferencing programs in Indonesia. A video conferencing application is a multimedia program that generates audio and visual content to facilitate real-time, two-way communication between its users. Because of COVID-19, classes of all kinds are now being taken online. As a result, more people are turning to tools like video conferencing. Therefore, learning how to access student video conferencing software is crucial. The UTAUT 2 and Delone & McLean models will be integrated into the analysis. A total of 327 people answered the survey. Next, we used the PLS-SEM technique in smart pls 3.0 to analyze the data collected from the respondents. The R-Square value of 26.2% for the retention intent variable and 62.3% for the user satisfaction variable demonstrate that independent variables in the study can explain endogenous variables and that the remaining variance is influenced by factors external to the survey.
Investigating the Profile of Digital Readiness and Sustainability Development: An Explainable Clustering Pamuji, Agus; Susanty, Aries; Warsito, Budi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.545

Abstract

The level of digital readiness within Islamic Higher Education Institutions (IHEIs) has emerged as a critical concern, drawing increasing scholarly and institutional attention over the past five years. This study aims to examine the empirical relationship between two key dimensions: digital readiness, as reflected by the National Readiness Index (NRI), and progress toward the Sustainable Development Goals (SDGs). Data were collected from more than 20 IHEIs between 2023 and 2024 to support a sequential analytical approach. Pearson’s correlation coefficient was employed to identify associations between NRI-based digital readiness and SDG performance within the IHEI context. Subsequently, cluster analysis was conducted using the Duda–Hart Index, while the Pseudo T² statistic was applied to validate the robustness of the clustering outcomes. A cartographic visualization was also generated to illustrate variations across readiness and sustainability clusters. The results indicate a considerable disparity between digital readiness and sustainability among IHEIs. Only a limited number of institutions demonstrate consistent performance in both areas, suggesting that effective leadership and strategic investment in digital infrastructure are essential prerequisites for achieving sustainable institutional transformation.
Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application Eka Rahmawati; Adi Wibowo; Budi Warsito
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Supporting children’s early development requires consistent attention, ensuring their growth aligns with health standards. PrimaKu is one of the mobile applications developed by the Indonesian Pediatric Society. That application was created to assist parents in recording developmental milestones, monitoring immunization schedules, and accessing practical health information. This study investigates user perceptions of the application by analyzing publicly available reviews and ratings from the Google Play Store. Four supervised machine learning algorithms were applied to classify the sentiment expressed in the reviews: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. Among the models tested, SVM achieved the highest classification accuracy (81%), followed by Random Forest (77%), Decision Tree (74%), and Naive Bayes (73%). Precision, recall, and F1-score were also used to evaluate the performance of each model. The results highlight the relevance of machine learning in capturing and interpreting user sentiment toward digital health tools. Further exploration of deep learning architectures is encouraged to enhance classification accuracy and understanding of features.
Enhanced Robustness in Image Classification through DistortionMix: A Hybrid Distortion-Based Augmentation Technique Fadhilah, Husni; Warsito, Budi; Faridah, Hasna
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1558

Abstract

Deep neural networks perform well on clean image classification tasks but often fail under common corruptions and distribution shifts. This paper introduces DistortionMix, a lightweight hybrid distortion-based augmentation technique designed to improve model robustness. It randomly applies contrast variation, Gaussian noise, or impulse noise to training images, enhancing data diversity and encouraging resilient feature learning. We evaluate DistortionMix on CIFAR-10 (clean) and CIFAR-10-C (corrupted), which includes 19 corruption types at five severity levels. A variety of architectures e.g ResNet, DenseNet, EfficientNet, MobileNet, VGG, AlexNet, GoogleNet, and ViT are fine-tuned with and without DistortionMix. Experimental results show that DistortionMix improves corrupted accuracy by up to 13.8%, while maintaining or slightly improving clean accuracy. Among all models, ViT-Base (timm) achieves the highest robustness, reaching 89.4% on severe corruptions and 97.43% on clean data. These findings highlight DistortionMix as a simple yet effective strategy for enhancing out-of-distribution generalization. Future work includes extending distortion types, developing adaptive augmentation policies, and evaluating performance on real-world corrupted datasets. Source code: github.com/HusniFadhilah/DistortionMix.
Hybrid Stacking Model for Web Attack Classification Using LightGBM, Random Forest, and MLP Fadli Dony Pradana; Farikhin; Budi Warsito
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The research presents a stacking-based hybrid intrusion detection framework for web application attacks, addressing the persistent limitation that minority classes, including Brute Force, Cross-Site Scripting (XSS), and Structured Query Language (SQL) Injection, are frequently underdetected in conventional Intrusion Detection Systems (IDS) due to severe class imbalance. The proposed architecture combines LightGBM and Random Forest as base learners, while a Multi-Layer Perceptron (MLP) functions as the meta-learner. The framework is supported by rigorous preprocessing, ANOVA F-testbased feature selection, and domain-informed augmentation of critical traffic features, such as Flow Inter-Arrival Time (IAT) Min, Init Win bytes forward, and Backward (Bwd) Packets/s, through optimized weighting strategies. Evaluation on the CICIDS-2017 web attack subset using 10-fold stratified cross-validation shows that the proposed model improves the macro F1-Score from 0.62 ± 0.004 to 0.76 ± 0.003 and achieves a binary accuracy of 99.67% with a macro F1 of 0.94. The observed performance gains are statistically significant (p < 0.001), confirming the robustness of the framework. These findings indicate that targeted feature engineering and heterogeneous stacking substantially improve minority-attack detection while preserving majority-class performance. In addition, the framework demonstrates sub-millisecond inference time, highlighting its practical suitability for real-time IDS deployment in resource-constrained and high-throughput operational cybersecurity environments. The proposed design also offers methodological generalizability for broader anomaly detection tasks in dynamic network environments, where reliable recognition of low-frequency but high-impact attack patterns remains increasingly critically important.
Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Pamuji Agus Pamuji Agus Pamuji Agus Rusgiyono Ahmad Lubis Ghozali Ahmed, Kamil Alan Prahutama Anik Sarminingsih Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arief Rachman Hakim Aries Susanty Aries Susanty Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Azizah Bagus Dwi Saputra Bayastura, Shahnilna Fitrasha Bayu Surarso Bayu Surarso Bimastyaji Surya Ramadhan Bowo Winarno Budiyono Budiyono Calvin, Esagu John Catur Edi Widodo Chrisna Suhendi Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Dyna Marisa Khairina eka rahmawati Eka Rahmawati Ekky Rosita Singgih Wigati Endang Fatmawati Endang Fatmawati Fachry Abda El Rahman Fadhilah, Husni Fadli Dony Pradana Faisal Fikri Utama Faliha Muthmainah Faridah, Hasna Fath Ezzati Kavabilla Fatiya Nur Umma Ferry Hermawan Fiqria Devi Ariyani Firdonsyah, Arizona Gayuh Kresnawati Gertrude, Akello Ghifar Rahman Gregorius Anung Hanindito Handayani, Sri Hanif Kusumasasmita Haritsa, Rifda Tsaqifarani Harjum Muharam Hasbi Yasin Hendri Setyawan Henny Widayanti, Henny Heriyanto Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Iut Tri Utami Jafron Wasiq Hidayat Jumi Juwanda, Farikhin Kadarrisman, Vincensius Gunawan Slamet Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maharani, Chintya Ayu Mahrus Ali Maori, Nadia Annisa Maryono Maryono Maryono Maryono Masruroh, Fitriana Maulida Najwa, Maulida Mifta Ardianti Moch. Abdul Mukid Mochamad Arief Budihardjo Moh Ali Fikri mohamad jamil Muhammad Shodiq Muliyadi Muliyadi Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nisa Afida Izati Noor Azizah Nur Dihyah Nur Fitriyah Nur Rochman Nurcahyanti, Tri Meida Nurul Fajrin Aghentika Nurul Hidayati Oktavia, Cintika Oky Dwi Nurhayati Pandu Anggara Paul, Gudoyi M Perdana, Ery Purwanto Purwanto Puspita Kartikasari Putri, Nitami Lestari R Rizal Isnanto R. Rizal Isnanto RACHMAN HAKIM, ARIEF Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmat Gernowo Rahmatul Akbar Rani Zulaikha Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riva Amrulloh Riza Rizqi Robbi Arisandi Royani, Noorhanida Rukun Santoso Rully Rahadian Safitri, Adila Salma Farah Aliyah Sang Nur Cahya Widiutama Sari, Juwita Dwinda Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sudarno utomo Sugito Sugito Sulardjaka Sulardjaka Suparti Suparti Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tonni Agustiono Kurniawan Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Whisnumurti Adhiwibowo Wibowo, Catur Edi Winahyu Handayani Yanuar Yoga Prasetyawan Yundari, Yundari